Jianhua Joshua Yang

Jianhua Joshua Yang
University of Southern California | USC · Department of Electrical Engineering

Doctor of Philosophy

About

266
Publications
107,137
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28,553
Citations
Citations since 2016
142 Research Items
22980 Citations
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201620172018201920202021202201,0002,0003,0004,0005,000

Publications

Publications (266)
Article
The increasing interests in analog computing nowadays call for multi‐purpose analog computing platforms with reconfigurability. The advancement of analog computing, enabled by novel electronic elements like memristors, has shown its potential to sustain the exponential growth of computing demand in the new era of analog data deluge. Here, we experi...
Article
Interface‐type (IT) resistive switching (RS) memories are promising for next generation memory and computing technologies owing to the filament‐free switching, high on/off ratio, low power consumption, and low spatial variability. Although the switching mechanisms of memristors have been widely studied in filament‐type devices, they are largely unk...
Article
Full-text available
In this work, the effect of rapid thermal annealing (RTA) temperature on the ferroelectric polarization in zirconium-doped hafnium oxide (HZO) was studied. To maximize remnant polarization (2P r ), in-plane tensile stress was induced by tungsten electrodes under optimal RTA temperatures. We observed an increase in 2P r with RTA temperature, likely...
Preprint
Full-text available
Neural networks based on memristive devices have shown potential in substantially improving throughput and energy efficiency for machine learning and artificial intelligence, especially in edge applications. Because training a neural network model from scratch is very costly, it is impractical to do it individually on billions of memristive neural...
Article
Neuromorphic Computing Different from memory applications where only the static ending states are used, neuromorphic computing with memristors utilizes their dynamical switching process itself, which calls for a comprehensive dynamical memristor model. Such a model shown here can reproduce the long‐term memory of drift memristors and the short‐term...
Article
In-memory computing chips based on magnetoresistive random-access memory devices can provide energy-efficient hardware for machine learning tasks.
Article
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Timing Selector In article number 2100072, Mingyi Rao, Zhongrui Wang, J. Joshua Yang, and co-workers conceptualize and demonstrate a timing selector, which can utilize its nonlinear relation between switching time and voltage, instead of current and voltage in traditional selectors, to block sneaking current in memristive crossbar arrays. The opera...
Article
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Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer te...
Chapter
In this work, the resistance switching mechanism of Reset process has been suggested through the statistics of the reset voltage and the reset current, which is consistent with the thermal-activated dissolution model. Furthermore, the variability nature of the switching parameters has been analyzed by screening the statistical data into different r...
Chapter
Computing hardware systems based on memristors can eliminate the data shuttling between the memory and processing units, offer massive parallelism, and hence promise substantial boost in computing throughput and energy efficiencies. However, such non-von Neumann computational approach imposes high requirements on specific characteristics of memrist...
Preprint
Full-text available
The ever-increasing amount of data from ubiquitous smart devices fosters data-centric and cognitive algorithms. Traditional digital computer systems have separate logic and memory units, resulting in a huge delay and energy cost for implementing these algorithms. Memristors are programmable resistors with a memory, providing a paradigm-shifting app...
Article
Full-text available
Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices...
Article
A seemingly disordered network of nanowires governed by thermodynamics is used as the physical ‘reservoir’ in a memristive implementation of reservoir computing to process spatiotemporal information.
Article
Full-text available
Sneak path current is a fundamental issue and a major roadblock to the wide application of memristor crossbar arrays. Traditional selectors such as transistors compromise the 2D scalability and 3D stack‐ability of the array, while emerging selectors with highly nonlinear current–voltage relations contradict the requirement of a linear current–volta...
Article
Different from nonvolatile memory applications, neuromorphic computing applications utilize not only the static conductance states but also the switching dynamics for computing, which calls for compact dynamical models of memristive devices. In this work, a generalized model to simulate diffusive and drift memristors with the same set of equations...
Article
Full-text available
As semiconductor technology enters the more than Moore era, there exists an apparent contradiction between the rapidly growing demands for data processing and the visible inefficiency rooted in traditional computing architecture. Neuromorphic systems hold great prospects in enabling a new generation of computing paradigm that can address this issue...
Preprint
Full-text available
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In this architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is...
Conference Paper
Implementing synaptic weights using tunable conductance of memristors offers significant power and computing throughput improvements through in-memory analog computing. Hardware-based perceptron implementation with memristor crossbar arrays has attracted increased interest in recent years. However, all the previous memristor-based perceptron demons...
Article
Full-text available
Memristor devices have been extensively studied as one of the most promising technologies for next-generation non-volatile memory. However, for the memristor devices to have a real technological impact, they must be densely packed in a large crossbar array (CBA) exceeding Gigabytes in size. Devising a selector device that is CMOS compatible, 3D sta...
Article
Full-text available
Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of...
Article
Full-text available
In the nervous system, dendrites, branches of neurons that transmit signals between synapses and soma, play a critical role in processing functions, such as nonlinear integration of postsynaptic signals. The lack of these critical functions in artificial neural networks compromises their performance, for example in terms of flexibility, energy effi...
Article
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Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-ce...
Article
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To tackle important combinatorial optimization problems, a variety of annealing-inspired computing accelerators, based on several different technology platforms, have been proposed, including quantum-, optical- and electronics-based approaches. However, to be of use in industrial applications, further improvements in speed and energy efficiency are...
Article
Full-text available
Early processing of visual information takes place in the human retina. Mimicking neurobiological structures and functionalities of the retina provides a promising pathway to achieving vision sensor with highly efficient image processing. Here, we demonstrate a prototype vision sensor that operates via the gate-tunable positive and negative photore...
Article
Full-text available
Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter...
Article
Full-text available
Constructing a computing circuit in three dimensions (3D) is a necessary step to enable the massive connections and efficient communications required in complex neural networks. 3D circuits based on conventional complementary metal–oxide–semiconductor transistors are, however, difficult to build because of challenges involved in growing or stacking...
Article
Full-text available
Memristor devices could realize their full scaling (2D) and stacking (3D) potential if vertically integrated with a two‐terminal selector in a one selector one memristor (1S1R) crossbar array. However, for a 1S1R‐integrated device to function properly, memristor and selector should be compatible in terms of their material composition and electrical...
Article
Full-text available
The switching parameters and device performance of memristors are predominately determined by their mobile species and matrix materials. Devices with oxygen or oxygen vacancies as the mobile species usually exhibit a great retention but also need a relatively high switching current (e.g., >30 µA), while devices with Ag or Cu as cation mobile specie...
Preprint
Full-text available
Early processing of visual information takes place in the human retina. Mimicking neurobiological structures and functionalities of the retina provide a promising pathway to achieving vision sensor with highly efficient image processing. Here, we demonstrate a prototype vision sensor that operates via the gate-tunable positive and negative photores...
Article
This article provides a review of current development and challenges in brain-inspired computing with memristors. We review the mechanisms of various memristive devices that can mimic synaptic and neuronal functionalities and survey the progress of memristive spiking and artificial neural networks. Different architectures are compared, including sp...
Article
Full-text available
Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks1–4. However, convolutional neural networks (CNNs)—one of the most important models for image recognition5—have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor devi...
Article
The rapid increase in information in the big-data era calls for changes to information-processing paradigms, which, in turn, demand new circuit-building blocks to overcome the decreasing cost-effectiveness of transistor scaling and the intrinsic inefficiency of using transistors in non-von Neumann computing architectures. Accordingly, resistive swi...
Article
Full-text available
Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the e...
Chapter
Development of hardware-based spiking neural networks calls for novel building blocks, such as artificial neurons. This could lead to the development of systems with reduced power consumption, fault tolerance, and biomimetic artificial intelligence. Memristors, which are devices with a signal history dependent resistance, are realized via various p...
Article
Full-text available
As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the...
Article
Full-text available
Owing to their attractive application potentials in both non-volatile memory and unconventional computing, memristive devices have drawn substantial research attention in the last decade. However, major roadblocks still remain in device performance, especially concerning relatively large parameter variability and limited cycling endurance. The resp...
Preprint
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The hardware and software foundations laid in the first half of the 20th Century enabled the computing technologies that have transformed the world, but these foundations are now under siege. The current computing paradigm, which is the foundation of much of the current standards of living that we now enjoy, faces fundamental limitations that are e...
Article
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Neuromorphic computers based on analogue neural networks aim to substantially lower computing power by reducing the need to shuttle data between memory and logic units. Artificial synapses containing nonvolatile analogue conductance states enable direct computation using memory elements; however, most nonvolatile analogue memories require high writ...
Article
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Reservoir Computing is a framework that can extract features from a temporal input into a higher dimension feature space. The reservoir is followed by a readout layer that can analyze the extracted features to accomplish tasks such as inference and classification. Reservoir Computing systems inherently possess an advantage since the training is onl...
Article
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Non-volatile computing-in-memory (nvCIM) could improve the energy efficiency of edge devices for artificial intelligence applications. The basic functionality of nvCIM has recently been demonstrated using small-capacity memristor crossbar arrays combined with peripheral readout circuits made from discrete components. However, the advantages of the...
Article
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The explosive growth of machine learning is largely due to the recent advancements in hardware and architecture. The engineering of network structures, taking advantage of the spatial or temporal translational isometry of patterns, naturally leads to bio-inspired, shared-weight structures such as convolutional neural networks, which have markedly r...
Article
Neuromorphic computers based on analog neural networks aim to substantially lower computing power by reducing the need to shuttle information between memory and logic units. Artificial synapses containing analog, non-volatile conductance states enable direct computation using memory elements; however, most non-volatile analog memories require high...
Article
Resistance random-access memory (RRAM) is a promising candidate for both the next-generation non-volatile memory and the key element of neural networks. In this article, different types of Mott-transition (the transition between the Mott insulator and metallic states) mechanisms and Mott-transition-based RRAM are reviewed. Mott insulators and some...
Article
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An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Article
Ionic floating-gate memories Digital implementations of artificial neural networks perform many tasks, such as image recognition and language processing, but are too energy intensive for many applications. Analog circuits that use large crossbar arrays of synaptic memory elements represent a low-power alternative, but most devices cannot update the...
Article
With their working mechanisms based on ion migration, the switching dynamics and electrical behaviour of memristive devices resemble those of synapses and neurons, making these devices promising candidates for brain-inspired computing. Built into large-scale crossbar arrays to form neural networks, they perform efficient in-memory computing with ma...
Preprint
Full-text available
We describe a hybrid analog-digital computing approach to solve important combinatorial optimization problems that leverages memristors (two-terminal nonvolatile memories). While previous memristor accelerators have had to minimize analog noise effects, we show that our optimization solver harnesses such noise as a computing resource. Here we descr...
Article
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Reinforcement learning algorithms that use deep neural networks are a promising approach for the development of machines that can acquire knowledge and solve problems without human input or supervision. At present, however, these algorithms are implemented in software running on relatively standard complementary metal–oxide–semiconductor digital pl...
Article
Biorealistic spiking neural networks (SNN) are believed to hold promise for further energy improvement over artificial neural networks (ANNs). However, it is difficult to implement SNNs in hardware, in particular the complicated algorithms that ANNs can handle with ease. Thus, it is natural to look for a middle path by combining the advantages of t...
Article
italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s note: Emerging non-CMOS devices have various advantages and challenges. It is important to undertake device-architecture codesign to harness the true benefits of beyond-CMOS devices. This article discusses the landscape of designing computing...
Article
A highly reliable memristive device based on tantalum‐doped silicon oxide is reported, which exhibits high uniformity, robust endurance (≈1 × 10⁹ cycles), fast switching speed, long retention, and analog conductance modulation. Devices with junction areas ranging from microscale to as small as 60 × 15 nm² are fabricated and electrically characteriz...
Article
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The memristor1,2 is a promising building block for next-generation non-volatile memory³, artificial neural networks4–7 and bio-inspired computing systems8,9. Organizing small memristors into high-density crossbar arrays is critical to meet the ever-growing demands in high-capacity and low-energy consumption, but this is challenging because of diffi...
Article
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Deep neural networks are increasingly popular in data-intensive applications, but are power-hungry. New types of computer chips that are suited to the task of deep learning, such as memristor arrays where data handling and computing take place within the same unit, are required. A well-used deep learning model called long short-term memory, which c...
Article
Full-text available
Vector matrix multiplication computation underlies major applications in machine vision, deep learning, and scientific simulation. These applications require high computational speed and are run on platforms that are size, weight, and power constrained. With the transistor scaling coming to an end, existing digital hardware architectures will not b...
Article
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The data included in this article provides additional supplementary information on our recent publication describing "Inducing tunable switching behavior in a single memristor" [1]. Analyses of micro/nano-structural and compositional changes induced in a resistive oxide memory during resistive switching are carried out. Chromium doped strontium tit...